Technology Trends That Empower Innovation

Advances in 18 technology areas are empowering and inspiring manufacturers. Open standards, more powerful desktop computers and lower-cost software make design, modeling and automatic code generation for PLCs and PACs practical for improving automation. Other technologies go beyond problem-solving to achieve productivity and performance enhancements. Here’s a look at advances in 18 technology areas that are worth paying attention to:

AI, ML and expert systems; Cloud computing; Hyperautomation; Low-code/no-code platforms; Edge computing platforms; Modular design and programming; BioPhorum activity; Semantic/contextual data; Communications; Multiplatform closed ecosystems; Open source; IEC 61499; Eclipse Foundation 4diac/Forte; OPC Foundation field level communications; Digital twins; Intelligent sensors; Spatial computing/intelligent vision; Connected worker technology; Remote expert services; Robotics.

AI, ML and expert systems

The commercial use of artificial intelligence is accelerating at all levels with the wide commercial application of AI, natural language processing, machine learning and other expert systems. Increased processing power at lower cost is accelerating the technology. It is tempting to apply new technology immediately but as with any technology, these are new tools that need to be understood and applied properly; they are not instant “silver bullets” to solve all problems and increase operations efficiencies. The quality and value of AI applications depend directly on internal algorithms and data sources. In the context of industrial automation and controls, poorly applied AI can have negative outcomes impacting performance, personnel and plant safety.

The European Commission AI ACT Legal Framework notes: “What does ‘reliable’ mean in the AI context? We speak of a ‘reliable’ AI application if it is built in compliance with data protection, makes unbiased and comprehensible decisions, and can be controlled by humans.” The AI ACT Regulatory Framework defines four levels of risk for AI systems: unacceptable risk, high risk, limited risk and minimal risk. Mission-critical industrial control and automation applications are within the AI ACT high-risk category. AI systems identified as high-risk include AI technology used in:

• Critical infrastructures (e.g., transport), that could put the life and health of citizens at risk.
• Educational or vocational training, that may determine the access to education and professional course of someone’s life (e.g., scoring of exams).
• Safety components of products (e.g., AI application in robot-assisted surgery).
• Employment, management of workers and access to self-employment (e.g., CV-sorting software for recruitment procedures).
• Essential private and public services (e.g., credit scoring denying citizens opportunity to obtain a loan).
• Law enforcement that may interfere with peoples’ fundamental rights (e.g., evaluation of the reliability of evidence).
• Migration, asylum, and border control management (e.g., automated examination of visa applications).
• Administration of justice and democratic processes (e.g., AI solutions to search for court rulings).

Properly applied AI, ML and expert systems offer industrial companies enormous potential to significantly cut operating expenses and improve staff efficiencies, quality, productivity, operations and reduce maintenance and repair costs. AI technologies help achieve the goals of all industrial automation to increase productivity and efficiency. AI industrial applications properly designed with the right data can more effectively handle unforeseen scenarios in complex and rapidly changing environments based on patterns and trends in the data without being explicitly programmed for every possible scenario with little to no human interaction.

The goals of AI applications should be in line with the company’s overall strategy and then define potential AI use cases for evaluation and prioritization for projects. There are an increasing number of no-code, self-serve software tools simplifying the application of these technologies by industrial subject matter experts rather than data scientists. Industrial automation and control systems have a wealth of data that can be used more effectively with these technologies. In addition, AI processor chips enable high-performance applications to run within controllers and edge computers for demanding applications.

Server and cloud AI/ML/expert system solutions are suitable for a wide range of applications, but network communication speed and latency factors pose limitations for many real-time industrial and process applications that are overcome with AI chips embedded in industrial edge devices and sensors. There are offerings in the market including Nvidia, Intel Myriad-X, Google Edge TPU and Hailo. These new technologies are proven in other areas including video analytics with image recognition and related applications.

These chips can be applied using plugin add-on board modules that are aggressively priced, conforming to the popular M.2 and mPCIe connector standards found in many computers including embedded industrial PCs, adding high-performance AI processing without degrading other applications in the computer. This is analogous to early PC coprocessor add-ons to achieve high-performance floating-point mathematical calculation performance and video display coprocessors to achieve high-resolution/performance graphics.

Cloud computing

Cloud computing is delivering efficient and powerful applications at a lower cost. These applications are being applied to improve manufacturing with technology solutions from suppliers including Amazon Web Services (AWS) and Microsoft architectures—important industrial digitalization building blocks from sensor to enterprise and cloud. Cloud software architectures and tools built on open standards are highly refined and easy to use to develop a wide range of applications including historians, AI, expert systems, machine learning (ML) and digital twins. Evidence of the commitment to the integration of the entire manufacturing business is membership and participation in the OPC Foundation by technology companies including AWS, Microsoft, IBM and Capgemini.

Cloud applications are providing many functions previously only available with onsite systems. This is particularly important for small and medium-sized manufacturers that did not have the financial strength to make the large investment required for onsite systems. Cloud applications provide small and medium manufacturers with the functions previously only available to large companies to increase efficiency and profits.

In the U.S., companies with fewer than 100 employees make up more than 94% of all U.S. manufacturers. In Europe, there are approximately 22.6 million small and medium-sized enterprises (SMEs) in the European Union in 2021. For example, comprehensive system as a service (SaaS) manufacturing business solutions is an efficient way to achieve integrated digitalization of all functions, including enterprise resource planning (ERP), manufacturing execution system (MES/MOM), quality management…

Connected worker technology

Employees can be empowered with mobile devices, giving them information and control capabilities that have traditionally been fixed in the control room to work more efficiently and effectively. Devices include smartphones, tablets and smart glasses incorporating front-facing high-definition cameras, audio and visual information. This capability has been available for some time, but the cost has become significantly lower driven by commercial and consumer products. New technology is enabling remote monitoring capabilities to improve operational effectiveness. This presents users with opportunities and challenges to be evaluated for practical applications.

The goal is to improve manufacturing or processing uptime and efficiency. Subject matter experts are becoming increasingly hard to find and companies need to find ways to use them more efficiently. The latest remote monitoring tools allow experts to analyze problems and abnormal situations and determine ways to improve and optimize operations without traveling to the site. Worker productivity and responsiveness are being improved with technologies that directly connect workers to manufacturing systems making them an informed integral part of production in real time. Mobile computing and communications technology cost reductions and increased performance continue to increase the ability to increase the capabilities and value of workers in production.

The connection of workers is being accelerated using the wide expanding range of commercial off-the-shelf technologies including voice and video headsets, Smart glasses and virtual reality devices and systems that are providing workers with productivity enhancers including:

• Manuals anywhere
• Equipment identification and lookup
• Real-time superimposed data
• Audiovisual linking to subject matter experts
• Direct access to production availability information

Remote expert services

Connectivity and edge processors empower suppliers to offer remote expert monitoring services. Experts and analytic software continuously monitor controllers and control systems for abnormal situations and advise site personnel of current problems or predictions of future problems. Control suppliers that offer these services have experts and software that can quickly detect issues with the controllers, components and software that they provide. Since most plants have equipment from multiple suppliers, the value of this service may be limited if the provider does not monitor all equipment and applications. In some general equipment and process control applications, contract experts can detect and advise on plant production issues.

Subject matter experts in specific manufacturing and process areas can be used on demand for special problems and issues. A big advantage of the services approach is a third party has a remote, 24/7 operations center to constantly monitor your systems. Some providers may collect performance analytics information to learn how machinery is performing and provide alerts when data falls outside of predefined parameters. This requires the development of rules with input from plant staff because they understand the plant operations. Alternatively, manufacturing companies can run an inference engine with rules developed by plant staff that understand the dynamics of operations.

Ultimately, when most problems and issues are identified, someone needs to be onsite with the right tools, information and spare parts to get things working. Determining the best methods to achieve improved uptime and efficiency is the overall challenge.

Robotics

The cost and ease of use of robotics have changed dramatically, particularly with collaborative robots (cobots). More possibilities are being created with the growing trend of modular industrial robot components that can be used to assemble the optimal robot structures for different applications on an individual and flexible basis. In addition, easy-to-use software tools are allowing people and plants to directly define robot actions without programming.